Dataset Information

Alteration of Proteotranscriptomic Landscape Reveals the Transcriptional Regulatory Circuits Controlling Key-Signaling Pathways and Metabolic Reprogramming During Tumor Evolution.

ABSTRACT: The proteotranscriptomic landscape depends on the transcription, mRNA-turnover, translation, and regulated-destruction of proteins. Gene-specific mRNA-to-protein correlation is the consequence of the dynamic interplays of the different regulatory processes of proteotranscriptomic landscape. So far, the critical impact of mRNA and protein stability on their subsequent correlation on a global scale remained unresolved. Whether the mRNA-to-protein correlations are constrained by their stability and conserved across mammalian species including human is unknown. Moreover, whether the stability-dependent correlation pattern is altered in the tumor has not been explored. To establish the quantitative relationship between stability and correlation between mRNA and protein levels, we performed a multi-omics data integration study across mammalian systems including diverse types of human tissues and cell lines in a genome-wide manner. The current study illuminated an important aspect of the mammalian proteotranscriptomic landscape by providing evidence that stability-constrained mRNA-to-protein correlation follows a hierarchical pattern that remains conserved across different tissues and mammalian species. By analyzing the tumor and non-tumor tissues, we further illustrated that mRNA-to-protein correlations deviate in tumor tissues. By gene-centric analysis, we harnessed the hierarchical correlation patterns to identify altered mRNA-to-protein correlation in tumors and characterized the tumor correlation-enhancing and -repressing genes. We elucidated the transcriptional regulatory circuits controlling the correlation-enhancing and -repressing genes that are associated with metabolic reprogramming and cancer-associated pathways in tumor tissue. By tightly controlling the mRNA-to-protein correlation of specific genes, the transcriptional regulatory circuits may enable the tumor cells to evolve in varying tumor microenvironment. The mRNA-to-protein correlation analysis thus can serve as a unique approach to identify the pathways prioritized by the tumor cells at different clinical stages. The component of transcriptional regulatory circuits identified by the current study can serve as potential candidates for stage-dependent anticancer therapy.

SUBMITTER: Andrieux G

PROVIDER: S-EPMC7769845 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Alteration of Proteotranscriptomic Landscape Reveals the Transcriptional Regulatory Circuits Controlling Key-Signaling Pathways and Metabolic Reprogramming During Tumor Evolution.

Andrieux Geoffroy G Chakraborty Sajib S Das Tonmoy T Boerries Melanie M

Frontiers in cell and developmental biology 20201215

The proteotranscriptomic landscape depends on the transcription, mRNA-turnover, translation, and regulated-destruction of proteins. Gene-specific mRNA-to-protein correlation is the consequence of the dynamic interplays of the different regulatory processes of proteotranscriptomic landscape. So far, the critical impact of mRNA and protein stability on their subsequent correlation on a global scale remained unresolved. Whether the mRNA-to-protein correlations are constrained by their stability and ...[more]

PMID: 33384992

Similar Datasets

Project description:Type 2 diabetes mellitus (T2D) is a multifactorial and genetically heterogeneous disease which leads to impaired glucose homeostasis and insulin resistance. The advanced form of disease causes acute cardiovascular, renal, neurological and microvascular complications. Thus there is a constant need to discover new and efficient treatment against the disease by seeking to uncover various novel alternate signalling mechanisms that can lead to diabetes and its associated complications. The present study allows detection of molecular targets by unravelling their role in altered biological pathways during diabetes and its associated risk factors and complications. We have used an integrated functional networks concept by merging co-expression network and interaction network to detect the transcriptionally altered pathways and regulations involved in the disease. Our analysis reports four novel significant networks which could lead to the development of diabetes and other associated dysfunctions. (a) The first network illustrates the up regulation of TGFBRII facilitating oxidative stress and causing the expression of early transcription genes via MAPK pathway leading to cardiovascular and kidney related complications. (b) The second network demonstrates novel interactions between GAPDH and inflammatory and proliferation candidate genes i.e., SUMO4 and EGFR indicating a new link between obesity and diabetes. (c) The third network portrays unique interactions PTPN1 with EGFR and CAV1 which could lead to an impaired vascular function in diabetic nephropathy condition. (d) Lastly, from our fourth network we have inferred that the interaction of beta-catenin with CDH5 and TGFBR1 through Smad molecules could contribute to endothelial dysfunction. A probability of emergence of kidney complication might be suggested in T2D condition. An experimental investigation on this aspect may further provide more decisive observation in drug target identification and better understanding of the pathophysiology of T2D and its complications.

Dataset Information

Alteration of Proteotranscriptomic Landscape Reveals the Transcriptional Regulatory Circuits Controlling Key-Signaling Pathways and Metabolic Reprogramming During Tumor Evolution.

Publications

Alteration of Proteotranscriptomic Landscape Reveals the Transcriptional Regulatory Circuits Controlling Key-Signaling Pathways and Metabolic Reprogramming During Tumor Evolution.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets